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Towards automatic building extraction: Variational level set model using prior shape knowledge

机译:走向自动建筑物提取:使用先验形状知识的变化水平集模型

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摘要

A novel variational level set model for multiple-building extraction from a single remote image is proposed in this paper. Multi-competing shapes are considered together with the level set model, the curve evolution is constrained by the prior shape knowledge and the label function that dynamically indicates the region with which the prior shape should be compared. The building extraction is addressed through a level set image segmentation approach that involves the use of the label function, as well as the prior shape knowledge. In addition, the proposed model permits translation, scaling, and rotation of the prior shape. Experimental results and the qualitative and quantitative evaluations demonstrate the potential of the approach.
机译:本文提出了一种新的变分层次集模型,用于从单个远程图像中进行多建筑物提取。将多个竞争形状与水平集模型一起考虑,曲线的发展受到先验形状知识和动态标注应与先验形状进行比较的区域的标注功能的限制。通过级别集图像分割方法来解决建筑物提取问题,该方法涉及使用标签功能以及现有的形状知识。此外,提出的模型允许平移,缩放和旋转先前的形状。实验结果以及定性和定量评估证明了该方法的潜力。

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